Author

João R. Campos

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Preprint Aug 2026

Unreliable in Practice? A Comprehensive Study of Errors in LLM-Generated Code

It is observed that generated code often omits basic input validation or memory-safety checks, which can lead to overflows, resource exhaustion, or other reliability/security issues, and even the largest models frequently make simple mistakes.

Rodrigo Pato Nogueira, Marco Vieira, João R. Campos · 0 citations

Open access Jul 2026

PROBE: Benchmarking code generation in large language models

The findings show that, while LLMs achieve promising results, they struggle with harder problems and with programming languages that have fewer available resources for training, and they often fail due to fundamental and easily avoidable errors that underscore the unreliability of automatically generated code.

Rodrigo Pato Nogueira, Marco Vieira, João R. Campos · Empirical Software Engineeri... · 1 citation